Venn Diagram Overlap of DE Genes
Last updated: 2025-02-19
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Knit directory: CX5461_Project/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 0c8c19f | sayanpaul01 | 2025-02-19 | Added UpSet plots for DEGs across samples, drugs, concentrations, and |
| html | 0ea6c0c | sayanpaul01 | 2025-02-19 | Build site. |
| Rmd | bef2c81 | sayanpaul01 | 2025-02-19 | Added UpSet plots for DEGs across samples, drugs, concentrations, and |
| html | 1dcb0c8 | sayanpaul01 | 2025-02-19 | Build site. |
| Rmd | af51575 | sayanpaul01 | 2025-02-19 | Added Overlap of DEGs analysis and updated index. |
📌 overlap of DE genes
📌 Read and Process DEG Data
# Load DEGs Data
CX_0.1_3 <- read.csv("data/DEGs/Toptable_CX_0.1_3.csv")
CX_0.1_24 <- read.csv("data/DEGs/Toptable_CX_0.1_24.csv")
CX_0.1_48 <- read.csv("data/DEGs/Toptable_CX_0.1_48.csv")
CX_0.5_3 <- read.csv("data/DEGs/Toptable_CX_0.5_3.csv")
CX_0.5_24 <- read.csv("data/DEGs/Toptable_CX_0.5_24.csv")
CX_0.5_48 <- read.csv("data/DEGs/Toptable_CX_0.5_48.csv")
DOX_0.1_3 <- read.csv("data/DEGs/Toptable_DOX_0.1_3.csv")
DOX_0.1_24 <- read.csv("data/DEGs/Toptable_DOX_0.1_24.csv")
DOX_0.1_48 <- read.csv("data/DEGs/Toptable_DOX_0.1_48.csv")
DOX_0.5_3 <- read.csv("data/DEGs/Toptable_DOX_0.5_3.csv")
DOX_0.5_24 <- read.csv("data/DEGs/Toptable_DOX_0.5_24.csv")
DOX_0.5_48 <- read.csv("data/DEGs/Toptable_DOX_0.5_48.csv")
# Extract Significant DEGs
DEG1 <- CX_0.1_3$Entrez_ID[CX_0.1_3$adj.P.Val < 0.05]
DEG2 <- CX_0.1_24$Entrez_ID[CX_0.1_24$adj.P.Val < 0.05]
DEG3 <- CX_0.1_48$Entrez_ID[CX_0.1_48$adj.P.Val < 0.05]
DEG4 <- CX_0.5_3$Entrez_ID[CX_0.5_3$adj.P.Val < 0.05]
DEG5 <- CX_0.5_24$Entrez_ID[CX_0.5_24$adj.P.Val < 0.05]
DEG6 <- CX_0.5_48$Entrez_ID[CX_0.5_48$adj.P.Val < 0.05]
DEG7 <- DOX_0.1_3$Entrez_ID[DOX_0.1_3$adj.P.Val < 0.05]
DEG8 <- DOX_0.1_24$Entrez_ID[DOX_0.1_24$adj.P.Val < 0.05]
DEG9 <- DOX_0.1_48$Entrez_ID[DOX_0.1_48$adj.P.Val < 0.05]
DEG10 <- DOX_0.5_3$Entrez_ID[DOX_0.5_3$adj.P.Val < 0.05]
DEG11 <- DOX_0.5_24$Entrez_ID[DOX_0.5_24$adj.P.Val < 0.05]
DEG12 <- DOX_0.5_48$Entrez_ID[DOX_0.5_48$adj.P.Val < 0.05]📌 Load Libraries
library(ggplot2)
library(ggVennDiagram)
library(UpSetR)📌 Across Drugs
📌Venn Diagram: CX-5461 vs VEH (0.1 µM)
venntest <- list(DEG1, DEG2, DEG3)
ggVennDiagram(
venntest,
category.names = c("CX_0.1_3", "CX_0.1_24", "CX_0.1_48"),
fill = c("red", "blue", "green")
) + ggtitle("CX-5461 Vs VEH 0.1 micromolar")+
theme(
plot.title = element_text(size = 16, face = "bold"), # Increase title size
text = element_text(size = 16) # Increase text size globally
)
| Version | Author | Date |
|---|---|---|
| 1dcb0c8 | sayanpaul01 | 2025-02-19 |
📌Venn Diagram: CX-5461 vs VEH (0.5 µM)
venntest1 <- list(DEG4, DEG5, DEG6)
ggVennDiagram(
venntest1,
category.names = c("CX_0.5_3", "CX_0.5_24", "CX_0.5_48"),
fill = c("red", "blue", "green")
) + ggtitle("CX-5461 Vs VEH 0.5 micromolar")+
theme(
plot.title = element_text(size = 16, face = "bold"), # Increase title size
text = element_text(size = 16) # Increase text size globally
)
| Version | Author | Date |
|---|---|---|
| 1dcb0c8 | sayanpaul01 | 2025-02-19 |
📌Venn Diagram: DOX vs VEH (0.1 µM)
venntest2 <- list(DEG7, DEG8, DEG9)
ggVennDiagram(
venntest2,
category.names = c("DOX_0.1_3", "DOX_0.1_24", "DOX_0.1_48"),
fill = c("red", "blue", "green")
) + ggtitle("DOX Vs VEH 0.1 micromolar")+
theme(
plot.title = element_text(size = 16, face = "bold"), # Increase title size
text = element_text(size = 16) # Increase text size globally
)
| Version | Author | Date |
|---|---|---|
| 1dcb0c8 | sayanpaul01 | 2025-02-19 |
📌Venn Diagram: DOX vs VEH (0.5 µM)
venntest3 <- list(DEG10, DEG11, DEG12)
ggVennDiagram(
venntest3,
category.names = c("DOX_0.5_3", "DOX_0.5_24", "DOX_0.5_48"),
fill = c("red", "blue", "green")
) + ggtitle("DOX Vs VEH 0.5 micromolar")+
theme(
plot.title = element_text(size = 16, face = "bold"), # Increase title size
text = element_text(size = 16) # Increase text size globally
)
| Version | Author | Date |
|---|---|---|
| 1dcb0c8 | sayanpaul01 | 2025-02-19 |
📌 Across Concentrations
📌Venn Diagram: 0.1 Micromolar
venntest7 <- list(DEG1, DEG2, DEG3, DEG7, DEG8, DEG9)
ggVennDiagram(
venntest7, label = "count",
category.names = c("CX_0.1_3", "CX_0.1_24", "CX_0.1_48", "DOX_0.1_3", "DOX_0.1_24", "DOX_0.1_48")
) + ggtitle("0.1 micromolar")+
theme(
plot.title = element_text(size = 16, face = "bold"), # Increase title size
text = element_text(size = 16) # Increase text size globally
)
| Version | Author | Date |
|---|---|---|
| 1dcb0c8 | sayanpaul01 | 2025-02-19 |
📌Venn Diagram: 0.5 Micromolar
venntest8 <- list(DEG4, DEG5, DEG6, DEG10, DEG11, DEG12)
ggVennDiagram(
venntest8, label = "count",
category.names = c("CX_0.5_3", "CX_0.5_24", "CX_0.5_48", "DOX_0.5_3", "DOX_0.5_24", "DOX_0.5_48")
) + ggtitle("0.5 micromolar")+
theme(
plot.title = element_text(size = 16, face = "bold"), # Increase title size
text = element_text(size = 16) # Increase text size globally
)
| Version | Author | Date |
|---|---|---|
| 1dcb0c8 | sayanpaul01 | 2025-02-19 |
📌 Across Timepoints
📌Venn Diagram: 3-hour Timepoint
venntest4 <- list(DEG1, DEG4, DEG7, DEG10)
ggVennDiagram(
venntest4, label_percent_digit = 2,
category.names = c("CX_0.1_3", "CX_0.5_3", "DOX_0.1_3", "DOX_0.5_3")
) + ggtitle("3hr")+
theme(
plot.title = element_text(size = 16, face = "bold"), # Increase title size
text = element_text(size = 16) # Increase text size globally
)
| Version | Author | Date |
|---|---|---|
| 1dcb0c8 | sayanpaul01 | 2025-02-19 |
📌Venn Diagram: 24-hour Timepoint
venntest5 <- list(DEG2, DEG5, DEG8, DEG11)
ggVennDiagram(
venntest5, label_percent_digit = 2,
category.names = c("CX_0.1_24", "CX_0.5_24", "DOX_0.1_24", "DOX_0.5_24")
) + ggtitle("24hr")+
theme(
plot.title = element_text(size = 16, face = "bold"), # Increase title size
text = element_text(size = 16) # Increase text size globally
)
| Version | Author | Date |
|---|---|---|
| 1dcb0c8 | sayanpaul01 | 2025-02-19 |
📌Venn Diagram: 48-hour Timepoint
venntest6 <- list(DEG3, DEG6, DEG9, DEG12)
ggVennDiagram(
venntest6, label_percent_digit = 2,
category.names = c("CX_0.1_48", "CX_0.5_48", "DOX_0.1_48", "DOX_0.5_48")
) + ggtitle("48hr")+
theme(
plot.title = element_text(size = 16, face = "bold"), # Increase title size
text = element_text(size = 16) # Increase text size globally
)
| Version | Author | Date |
|---|---|---|
| 1dcb0c8 | sayanpaul01 | 2025-02-19 |
📌 Overlap of DEGs (Upset Plot)
📌 Loading data
# Extract Significant DEGs
# Create a list of DEGs for each sample
DEG_list <- list(
CX_0.1_3 = CX_0.1_3$Entrez_ID[CX_0.1_3$adj.P.Val < 0.05],
CX_0.1_24 = CX_0.1_24$Entrez_ID[CX_0.1_24$adj.P.Val < 0.05],
CX_0.1_48 = CX_0.1_48$Entrez_ID[CX_0.1_48$adj.P.Val < 0.05],
CX_0.5_3 = CX_0.5_3$Entrez_ID[CX_0.5_3$adj.P.Val < 0.05],
CX_0.5_24 = CX_0.5_24$Entrez_ID[CX_0.5_24$adj.P.Val < 0.05],
CX_0.5_48 = CX_0.5_48$Entrez_ID[CX_0.5_48$adj.P.Val < 0.05],
DOX_0.1_3 = DOX_0.1_3$Entrez_ID[DOX_0.1_3$adj.P.Val < 0.05],
DOX_0.1_24 = DOX_0.1_24$Entrez_ID[DOX_0.1_24$adj.P.Val < 0.05],
DOX_0.1_48 = DOX_0.1_48$Entrez_ID[DOX_0.1_48$adj.P.Val < 0.05],
DOX_0.5_3 = DOX_0.5_3$Entrez_ID[DOX_0.5_3$adj.P.Val < 0.05],
DOX_0.5_24 = DOX_0.5_24$Entrez_ID[DOX_0.5_24$adj.P.Val < 0.05],
DOX_0.5_48 = DOX_0.5_48$Entrez_ID[DOX_0.5_48$adj.P.Val < 0.05]
)
# Convert list to binary matrix
DEG_matrix <- fromList(DEG_list)
# Define order of sets
set_order <- names(DEG_list)📌 UpSet Plot of DEGs Across Samples(Show all intersections till lowest size 5)
upset(
DEG_matrix,
sets = set_order, # Specify the exact order of sets
order.by = "freq", # Order intersections by frequency
main.bar.color = "blue", # Color for the intersection bars
matrix.color = "black", # Color for matrix dots
sets.bar.color = rainbow(length(DEG_list)), # Assign different colors to set size bars
keep.order = TRUE, # Keep the specified order of sets
number.angles = 0, # Angle of numbers in intersection size bars
point.size = 2.5, # Size of points in the matrix
text.scale = 1, # Scale for text elements
show.numbers = "yes" # Show intersection size numbers directly
)
| Version | Author | Date |
|---|---|---|
| 0ea6c0c | sayanpaul01 | 2025-02-19 |
📌 UpSet Plot of DEGs Across Samples(Show all intersections till last lowest size)
upset(
DEG_matrix,
sets = set_order, # Specify the exact order of sets
order.by = "freq", # Order intersections by frequency
main.bar.color = "blue", # Color for the intersection bars
matrix.color = "black", # Color for matrix dots
sets.bar.color = rainbow(length(DEG_list)), # Assign different colors to set size bars
keep.order = TRUE, # Keep the specified order of sets
number.angles = 0, # Angle of numbers in intersection size bars
point.size = 2.5, # Size of points in the matrix
text.scale = 1, # Scale for text elements
show.numbers = "yes", # Show intersection size numbers directly
nintersects = NA # Show all intersections including those with lowest size
)
| Version | Author | Date |
|---|---|---|
| 0ea6c0c | sayanpaul01 | 2025-02-19 |
📌 Drug Specific DE genes
📌 UpSet Plot: CX-5461 vs DOX
# Combine DEGs under CX-5461 and DOX
CX_DEGs <- unique(unlist(DEG_list[1:6]))
DOX_DEGs <- unique(unlist(DEG_list[7:12]))
# Create binary matrix for drug-specific DEGs
DEG_matrix_drug <- fromList(list(CX_5461 = CX_DEGs, DOX = DOX_DEGs))
# Generate the UpSet plot for drugs
upset(
DEG_matrix_drug,
sets = c("CX_5461", "DOX"),
order.by = "freq",
main.bar.color = "darkgreen",
point.size = 3,
text.scale = 1.5,
matrix.color = "purple",
sets.bar.color = c("blue", "red")
)
| Version | Author | Date |
|---|---|---|
| 0ea6c0c | sayanpaul01 | 2025-02-19 |
📌 Conc. Specific DE genes
📌 UpSet Plot: Concentration-Specific DEGs
# Combine DEGs under concentrations 0.1 and 0.5
DEG_0.1 <- unique(unlist(DEG_list[c(1, 2, 3, 7, 8, 9)]))
DEG_0.5 <- unique(unlist(DEG_list[c(4, 5, 6, 10, 11, 12)]))
# Create binary matrix for concentration-specific DEGs
DEG_matrix_concentration <- fromList(list(Concentration_0.1 = DEG_0.1, Concentration_0.5 = DEG_0.5))
# Generate the UpSet plot for concentration
upset(
DEG_matrix_concentration,
sets = c("Concentration_0.1", "Concentration_0.5"),
order.by = "freq",
main.bar.color = "darkorange",
matrix.color = "darkblue",
sets.bar.color = c("cyan", "magenta"),
text.scale = 1.5,
keep.order = TRUE
)
| Version | Author | Date |
|---|---|---|
| 0ea6c0c | sayanpaul01 | 2025-02-19 |
📌 Timepoints Specific DE genes
📌 UpSet Plot: Timepoint-Specific DEGs
# Combine DEGs under timepoints 3hr, 24hr, and 48hr
DEG_3hr <- unique(unlist(DEG_list[c(1, 4, 7, 10)]))
DEG_24hr <- unique(unlist(DEG_list[c(2, 5, 8, 11)]))
DEG_48hr <- unique(unlist(DEG_list[c(3, 6, 9, 12)]))
# Create binary matrix for timepoint-specific DEGs
DEG_matrix_timepoint <- fromList(list(Timepoint_3hr = DEG_3hr, Timepoint_24hr = DEG_24hr, Timepoint_48hr = DEG_48hr))
# Generate the UpSet plot for timepoints
upset(
DEG_matrix_timepoint,
sets = c("Timepoint_3hr", "Timepoint_24hr", "Timepoint_48hr"),
order.by = "freq",
main.bar.color = "darkgreen",
matrix.color = "darkred",
sets.bar.color = c("blue", "orange", "purple"),
text.scale = 1.5,
keep.order = TRUE
)
| Version | Author | Date |
|---|---|---|
| 0ea6c0c | sayanpaul01 | 2025-02-19 |
sessionInfo()R version 4.3.0 (2023-04-21 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 11 x64 (build 22631)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.utf8
[2] LC_CTYPE=English_United States.utf8
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.utf8
time zone: America/Chicago
tzcode source: internal
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] UpSetR_1.4.0 ggVennDiagram_1.5.2 ggplot2_3.5.1
[4] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] gtable_0.3.6 jsonlite_1.8.9 dplyr_1.1.4 compiler_4.3.0
[5] promises_1.3.0 tidyselect_1.2.1 Rcpp_1.0.12 stringr_1.5.1
[9] git2r_0.35.0 gridExtra_2.3 callr_3.7.6 later_1.3.2
[13] jquerylib_0.1.4 scales_1.3.0 yaml_2.3.10 fastmap_1.1.1
[17] plyr_1.8.9 R6_2.5.1 labeling_0.4.3 generics_0.1.3
[21] knitr_1.49 tibble_3.2.1 munsell_0.5.1 rprojroot_2.0.4
[25] bslib_0.8.0 pillar_1.10.1 rlang_1.1.3 cachem_1.0.8
[29] stringi_1.8.3 httpuv_1.6.15 xfun_0.50 getPass_0.2-4
[33] fs_1.6.3 sass_0.4.9 cli_3.6.1 withr_3.0.2
[37] magrittr_2.0.3 ps_1.8.1 digest_0.6.34 grid_4.3.0
[41] processx_3.8.5 rstudioapi_0.17.1 lifecycle_1.0.4 vctrs_0.6.5
[45] evaluate_1.0.3 glue_1.7.0 farver_2.1.2 whisker_0.4.1
[49] colorspace_2.1-0 rmarkdown_2.29 httr_1.4.7 tools_4.3.0
[53] pkgconfig_2.0.3 htmltools_0.5.8.1